Review of the particle swarm optimization method (PSO) for a global optimization problem

Karpenko,; Seliverstov,

doi:10.7463/00309.0116072

Cited by 6 publications

(5 citation statements)

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“…Taking into account the enumerated shortcomings of existing clustering methods, a new one was developed, which allows establishing border between foreground and background with higher accuracy. It combines the elements of particle swarm optimization method [16] and k-means method.…”

Section: B Processing Of the Image Preparation Resultsmentioning

confidence: 99%

Methods of Semantic Integrity Preservation in the Pattern Recognition Process

Kim

Matveeva

Viksnin

et al. 2019

International Journal of Embedded and Real-Time Communication Systems

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Computer vision is a wide area of theoretical research and technical methods connected with object detection, object tracking and object classification. In this article computer vision is considered in context of embedding it into automobiles in order to automate the road traffic process through video stream analysis. During road traffic it is vital to detect objects quickly and correctly, so the authors pay attention to the pattern recognition quality, especially to the visual information semantic integrity preservation. Their main purpose is to find the ways of its possible improvement respectively to three basic stages of the pattern recognition process. To avoid semantic integrity violations of information in the initial stage of the image analysis the authors propose normalization; in the second stage new clustering method was developed based on particle swarm optimization and k-means algorithm; in the final stage of the pattern recognition process the Haar cascade classifier was used with normalized training samples. The obtained image processing algorithm was implemented in case of blurred and noisy images and proved its effectiveness respectively to the visual information semantic integrity preservation.

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Section: B Processing Of the Image Preparation Resultsmentioning

confidence: 99%

Methods of Semantic Integrity Preservation in the Pattern Recognition Process

Kim

Matveeva

Viksnin

et al. 2019

International Journal of Embedded and Real-Time Communication Systems

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“…In order to resolve the issue of arbitrary choices on clustering parameters, authors decided to use some elements of particle swarm [19] optimization.…”

Section: Related Work and Theoretical Backgroundmentioning

confidence: 99%

“…xi are the normalized [19] values of RGB vector of the pixel x i ; c j is the centroid of the cluster C j ; r' cj ; g' cj ; b ' cj are the normalized values of RGB vector of the centroid c j . 5) Then for the pixel x i it is necessary to find a centroid c a , a ∈ [1; M] relative to which the square root of distance function value will be minimum and a centroid c b ; b ∈ [1; M] relative to which the value of color function value will be minimum.…”

Section: Related Work and Theoretical Backgroundmentioning

confidence: 99%

Image Clustering Method based on Particle Swarm Optimization

Kim

Matveeva

Viksnin

et al. 2018

Annals of Computer Science and Information Systems

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To implement efficient computer vision mechanisms, efficient image clustering methods are important. The paper elaborates a clustering method based on particle swarm optimization (PSO) which provides automatic establishment of clustering parameters. The developed PSO based clustering method was tested on 860 images for a car vision system and its results and contribution to the pattern recognition quality improvement were assessed in comparison with fuzzy C-means and k-means. The results do not differ significantly, but distinction in average time of work for these methods was noted. The PSO clustering method is faster than k-means and slower than fuzzy C-means. However, fuzzy C-means method does not guarantee correct results during the further analysis, so the PSO clustering method can be more efficient for implementation in computer vision systems.

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“…Particle swarm optimization is a computational optimization method that metaphorically imitates the patterns seen in fish schooling or bird flights. It involves modifying a set of candidate solutions in turn based on their fitness rating to obtain the best solution for a certain problem [4].…”

Section: Introductionmentioning

confidence: 99%

Image Steganography Based on Hybrid Salp Swarm Algorithm and Particle Swarm Optimization

2024

IJIES

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Image steganography is a secure communication technique that conceals confidential information within digital images, commonly used in military, digital, and intelligence forensics applications. It preserves data integrity and confidentiality by avoiding attention and suspicion. The goal of this technique is to select random positions within the image to hide data and enhance spatial image steganography. This paper presents a hybrid model based on the salp swarm algorithm (SSA) and particle swarm optimization (PSO). The model is used to locate the optimal position of a text message within a cover image and embed it, ensuring that the stego-image created is of high quality and resistant to specific image processing attacks. The first step involves using SSA to identify the finest pixels from the cover image. In the subsequent step, PSO is employed to choose the best possible bits from these pixels for message concealment. The proposed approach is evaluated on a set of images and compared to existing methods. Experimental findings demonstrate that the suggested system surpasses previous approaches. The suggested method achieved a PSNR of 84.32279 dB and an MSE of 0.00022505 on the Lena image, a PSNR of 84.68192 dB and an MSE of 0.00021362 on the Pepper image, a PSNR of 84.53469 dB and an MSE of 0.00022125 on Baboon, and a PSNR of 84.25440 dB and an MSE of 0.00023651 on the Airplane image. These results indicate higher visual quality and better preservation of data integrity compared to existing methods.

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Review of the particle swarm optimization method (PSO) for a global optimization problem

Cited by 6 publications

References 0 publications

Methods of Semantic Integrity Preservation in the Pattern Recognition Process

Methods of Semantic Integrity Preservation in the Pattern Recognition Process

Image Clustering Method based on Particle Swarm Optimization

Image Steganography Based on Hybrid Salp Swarm Algorithm and Particle Swarm Optimization

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